Covid-19 Project

Investigation of the Data

In this project I used data associated with the Coronavirus pandemic from 2020. Here are three datasets named corona_confirmed.csv, corona_recovered.csv and corona_deaths.csv. For the entirety of this project, I’ll be using these datasets. You can find more recent versions of this data at Johns Hopkins’ data resource.

library(dplyr)
library(readr)
library(knitr)

# data loading
confirmed <- read_csv("corona_confirmed.csv")
deaths <- read_csv("corona_deaths.csv")
recovered <- read_csv("corona_recovered.csv")

# Inspection of the data
head(confirmed) %>% kable()

Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	1/28/20	1/29/20	1/30/20	1/31/20	2/1/20	2/2/20	2/3/20	2/4/20	2/5/20	2/6/20	2/7/20	2/8/20	2/9/20	2/10/20	2/11/20	2/12/20	2/13/20	2/14/20	2/15/20	2/16/20	2/17/20	2/18/20	2/19/20	2/20/20	2/21/20	2/22/20	2/23/20	2/24/20	2/25/20	2/26/20	2/27/20	2/28/20	2/29/20	3/1/20	3/2/20	3/3/20	3/4/20	3/5/20	3/6/20	3/7/20	3/8/20	3/9/20	3/10/20	3/11/20	3/12/20	3/13/20	3/14/20	3/15/20	3/16/20	3/17/20	3/18/20	3/19/20	3/20/20	3/21/20	3/22/20
NA	Thailand	15.0000	101.0000	2	3	5	7	8	8	14	14	14	19	19	19	19	25	25	25	25	32	32	32	33	33	33	33	33	34	35	35	35	35	35	35	35	35	37	40	40	41	42	42	43	43	43	47	48	50	50	50	53	59	70	75	82	114	147	177	212	272	322	411	599
NA	Japan	36.0000	138.0000	2	1	2	2	4	4	7	7	11	15	20	20	20	22	22	45	25	25	26	26	26	28	28	29	43	59	66	74	84	94	105	122	147	159	170	189	214	228	241	256	274	293	331	360	420	461	502	511	581	639	639	701	773	839	825	878	889	924	963	1007	1086
NA	Singapore	1.2833	103.8333	0	1	3	3	4	5	7	7	10	13	16	18	18	24	28	28	30	33	40	45	47	50	58	67	72	75	77	81	84	84	85	85	89	89	91	93	93	93	102	106	108	110	110	117	130	138	150	150	160	178	178	200	212	226	243	266	313	345	385	432	455
NA	Nepal	28.1667	84.2500	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	2
NA	Malaysia	2.5000	112.5000	0	0	0	3	4	4	4	7	8	8	8	8	8	10	12	12	12	16	16	18	18	18	19	19	22	22	22	22	22	22	22	22	22	22	22	22	23	23	25	29	29	36	50	50	83	93	99	117	129	149	149	197	238	428	566	673	790	900	1030	1183	1306
British Columbia	Canada	49.2827	-123.1207	0	0	0	0	0	0	1	1	1	1	1	1	1	1	2	2	4	4	4	4	4	4	4	4	4	4	5	5	5	5	6	6	6	6	7	7	7	7	8	8	8	9	12	13	21	21	27	32	32	39	46	64	64	73	103	103	186	231	271	424	424

head(deaths) %>% kable()

Province/State	Country/Region	Lat	Long	2/13/20	2/14/20	2/15/20	2/16/20	2/17/20	2/18/20	2/19/20	2/20/20	2/21/20	2/22/20	2/23/20	2/24/20	2/25/20	2/26/20	2/27/20	2/28/20	2/29/20	3/1/20	3/2/20	3/3/20	3/4/20	3/5/20	3/6/20	3/7/20	3/8/20	3/9/20	3/10/20	3/11/20	3/12/20	3/13/20	3/14/20	3/15/20	3/16/20	3/17/20	3/18/20	3/19/20	3/20/20	3/21/20	3/22/20
NA	Thailand	15.0000	101.0000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
NA	Japan	36.0000	138.0000	1	1	1	1	1	1	1	1	1	1	1	1	1	2	4	4	5	6	6	6	6	6	6	6	6	10	10	15	16	19	22	22	27	29	29	29	33	35	40
NA	Singapore	1.2833	103.8333	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	2
NA	Nepal	28.1667	84.2500	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
NA	Malaysia	2.5000	112.5000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	2	2	3	4	10
British Columbia	Canada	49.2827	-123.1207	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	4	4	7	7	8	10	10

head(recovered) %>% kable()

Province/State	Country/Region	Lat	Long	1/26/20	1/27/20	1/28/20	1/29/20	1/30/20	1/31/20	2/1/20	2/2/20	2/3/20	2/4/20	2/5/20	2/6/20	2/7/20	2/8/20	2/9/20	2/10/20	2/11/20	2/12/20	2/13/20	2/14/20	2/15/20	2/16/20	2/17/20	2/18/20	2/19/20	2/20/20	2/21/20	2/22/20	2/23/20	2/24/20	2/25/20	2/26/20	2/27/20	2/28/20	2/29/20	3/1/20	3/2/20	3/3/20	3/4/20	3/5/20	3/6/20	3/7/20	3/8/20	3/9/20	3/10/20	3/11/20	3/12/20	3/13/20	3/14/20	3/15/20	3/16/20	3/17/20	3/18/20	3/19/20	3/20/20	3/21/20	3/22/20
NA	Thailand	15.0000	101.0000	2	2	5	5	5	5	5	5	5	5	5	5	5	10	10	10	10	10	12	12	12	14	15	15	15	15	17	17	21	21	22	22	22	28	28	28	31	31	31	31	31	31	31	31	33	34	34	35	35	35	35	41	42	42	42	42	44
NA	Japan	36.0000	138.0000	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	4	9	9	9	9	12	12	12	13	18	18	22	22	22	22	22	22	22	22	32	32	32	43	43	43	46	76	76	76	101	118	118	118	118	118	144	144	144	150	191	232	235
NA	Singapore	1.2833	103.8333	0	0	0	0	0	0	0	0	0	0	0	0	0	2	2	2	9	15	15	17	18	18	24	29	34	34	37	37	51	51	53	62	62	62	72	72	78	78	78	78	78	78	78	78	78	96	96	97	105	105	109	114	114	114	124	140	144
NA	Nepal	28.1667	84.2500	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
NA	Malaysia	2.5000	112.5000	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	3	3	3	3	7	7	7	13	15	15	15	15	15	18	18	18	18	18	18	18	18	22	22	22	22	23	24	24	24	26	26	26	35	42	42	49	60	75	87	114	139
British Columbia	Canada	49.2827	-123.1207	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	3	3	3	3	3	3	3	3	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4

Looking At March 22nd, 2020

The format of these three data frames are all the same; each row contains information about the number of cases in a certain province, state, or country. Every column (other than the columns containing the latitude, longitude, and country name) represents a date. We have data starting on January 22nd, 2020 and ending on March 22nd, 2020.

Let’s find the total number of confirmed cases on March 22nd, 2020.

# the total number of cases on March 22nd
count_confirmed <- confirmed %>%
                       select(`3/22/20`) %>%
                        sum()
count_confirmed

## [1] 335955

Filtering By Values

Let’s start to filter the data a bit. Three tasks are performed :

How many confirmed cases are there in countries on March 22nd that are north of the equator? (If a country is north of the equator, its latitude is greater than 0)
How many confirmed cases are there in March 22nd in Australia?

# countries in the northern hemisphere
northern_hemisphere_confirmed <- confirmed %>%
                            filter(Lat>0.0) %>%
                            select(`3/22/20`) %>%
                            sum()
northern_hemisphere_confirmed

## [1] 329794

# percentage
northern_hemisphere_confirmed_percent <- (northern_hemisphere_confirmed/count_confirmed)*100
northern_hemisphere_confirmed_percent

## [1] 98.16612

# Filter for Australia cases
australia_cases <- confirmed %>%
                   filter(`Country/Region` == "Australia") %>%
                   select(`3/22/20`) %>%
                   sum()
australia_cases

## [1] 1314

#percentage
australia_cases_percent <- (australia_cases / count_confirmed)*100
australia_cases_percent

## [1] 0.3911238

Grouping By Country

Here some countries have multiple rows of data. This happens when a country has information about specific states or provinces. While this information might be useful, it makes it a bit tricky to see the total number of cases by country.

New data frame is created containing one row for every Country/Region. Every column of those new rows should have the sum of the total number of cases for that country for every day.

In this case every column other than Lat, Long, and Province/State is needed. summarize_at() only works with numbers.

After creating this new data frame inspection is done. To confirm correct calculation, I found the row for Australia and confirmed the number of cases on March 22nd. That matches my results from the previous step

# Group by countries
case_by_country <- confirmed %>%
                      group_by(`Country/Region`) %>%
                      summarize_at(vars(-Lat, -Long, -`Province/State`), sum)
head(case_by_country)  %>% kable()

Country/Region	2/24/20	2/25/20	2/26/20	2/27/20	2/28/20	2/29/20	3/1/20	3/2/20	3/3/20	3/4/20	3/5/20	3/6/20	3/7/20	3/8/20	3/9/20	3/10/20	3/11/20	3/12/20	3/13/20	3/14/20	3/15/20	3/16/20	3/17/20	3/18/20	3/19/20	3/20/20	3/21/20	3/22/20
Afghanistan	1	1	1	1	1	1	1	1	1	1	1	1	1	4	4	5	7	7	7	11	16	21	22	22	22	24	24	40
Albania	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	10	12	23	33	38	42	51	55	59	64	70	76	89
Algeria	0	1	1	1	1	1	1	3	5	12	12	17	17	19	20	20	20	24	26	37	48	54	60	74	87	90	139	201
Andorra	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	2	39	39	53	75	88	113
Angola	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	2	2
Antigua and Barbuda	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1

# Checking calculation confirmation by Australia count
australia_count <- case_by_country %>%
                   filter(`Country/Region` == "Australia") %>%
                   select(`3/22/20`) %>%
                   sum()
australia_count

## [1] 1314

$Covid-19$ ## Investigating The Recovered Dataset

Same process of grouping by country using the recovered dataset is done again. - What percentage of the cases in the US have recovered on March 22nd?

# Group by countries
recovered_by_country <- recovered %>%
                      group_by(`Country/Region`) %>%
                      summarize_at(vars(-Lat, -Long, -`Province/State`), sum)
US_recovered_22 <- recovered_by_country %>%
                    filter(`Country/Region` == "US") %>%
                    select(`3/22/20`)
                    sum()

## [1] 0

US_recovered_22

## # A tibble: 1 x 1
##   `3/22/20`
##       <dbl>
## 1         0

Its a surprising result; are there really zero recovered cases in the US? Let’s take a closer look at the US row in the recovered table.

# Filtering to inspect the US row
recovered_by_country %>%
                    filter(`Country/Region` == "US")  %>% kable()

Country/Region	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	1/28/20	1/29/20	1/30/20	1/31/20	2/1/20	2/2/20	2/3/20	2/4/20	2/5/20	2/6/20	2/7/20	2/8/20	2/9/20	2/10/20	2/11/20	2/12/20	2/13/20	2/14/20	2/15/20	2/16/20	2/17/20	2/18/20	2/19/20	2/20/20	2/21/20	2/22/20	2/23/20	2/24/20	2/25/20	2/26/20	2/27/20	2/28/20	2/29/20	3/1/20	3/2/20	3/3/20	3/4/20	3/5/20	3/6/20	3/7/20	3/8/20	3/9/20	3/10/20	3/11/20	3/12/20	3/13/20	3/14/20	3/15/20	3/16/20	3/17/20	3/18/20	3/19/20	3/20/20	3/21/20	3/22/20
US	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	3	3	3	3	3	3	3	3	3	3	3	5	5	5	5	6	6	6	7	7	7	7	7	7	7	7	7	7	7	8	8	12	12	12	12	17	17	0	0	0	0	0

It seems like the number of recovered cases is steadily increasing to 17, until March 18th, when it suddenly drops back to 0. This is surprising, and not what is expected!

When I went back to Johns Hopkins’ resource we found a note saying that the data had moved into a different file.

We could report the maximum number of confirmed and recovered cases.

# Finding the maximum number of confirmed and recovered cases
max_recovered_US <- recovered_by_country %>%
                    filter(`Country/Region` == "US") %>%
                     select(-`Country/Region`) %>%
                    max()
max_recovered_US

## [1] 17

max_case_US <- case_by_country %>%
                    filter(`Country/Region` == "US") %>%
                     select(-`Country/Region`) %>%
                    max()
max_case_US

## [1] 33272

Transposing Data Frames

By transposing we could then find the maximum value of a country by simply selecting the appropriate column and finding the maximum value in that column. Let’s try that!

# Transposing the data frame
case_by_country <- case_by_country %>% 
                       t() %>%
                        as.data.frame()
head(case_by_country) %>% kable()

	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	V11	V12	V13	V14	V15	V16	V17	V18	V19	V20	V21	V22	V23	V24	V25	V26	V27	V28	V29	V30	V31	V32	V33	V34	V35	V36	V37	V38	V39	V40	V41	V42	V43	V44	V45	V46	V47	V48	V49	V50	V51	V52	V53	V54	V55	V56	V57	V58	V59	V60	V61	V62	V63	V64	V65	V66	V67	V68	V69	V70	V71	V72	V73	V74	V75	V76	V77	V78	V79	V80	V81	V82	V83	V84	V85	V86	V87	V88	V89	V90	V91	V92	V93	V94	V95	V96	V97	V98	V99	V100	V101	V102	V103	V104	V105	V106	V107	V108	V109	V110	V111	V112	V113	V114	V115	V116	V117	V118	V119	V120	V121	V122	V123	V124	V125	V126	V127	V128	V129	V130	V131	V132	V133	V134	V135	V136	V137	V138	V139	V140	V141	V142	V143	V144	V145	V146	V147	V148	V149	V150	V151	V152	V153	V154	V155	V156	V157	V158	V159	V160	V161	V162	V163	V164	V165	V166	V167	V168	V169	V170	V171
Country/Region	Afghanistan	Albania	Algeria	Andorra	Angola	Antigua and Barbuda	Argentina	Armenia	Australia	Austria	Azerbaijan	Bahamas, The	Bahrain	Bangladesh	Barbados	Belarus	Belgium	Benin	Bhutan	Bolivia	Bosnia and Herzegovina	Brazil	Brunei	Bulgaria	Burkina Faso	Cabo Verde	Cambodia	Cameroon	Canada	Cape Verde	Central African Republic	Chad	Chile	China	Colombia	Congo (Brazzaville)	Congo (Kinshasa)	Costa Rica	Cote d’Ivoire	Croatia	Cruise Ship	Cuba	Cyprus	Czechia	Denmark	Djibouti	Dominica	Dominican Republic	East Timor	Ecuador	Egypt	El Salvador	Equatorial Guinea	Eritrea	Estonia	Eswatini	Ethiopia	Fiji	Finland	France	Gabon	Gambia, The	Georgia	Germany	Ghana	Greece	Grenada	Guatemala	Guinea	Guyana	Haiti	Holy See	Honduras	Hungary	Iceland	India	Indonesia	Iran	Iraq	Ireland	Israel	Italy	Jamaica	Japan	Jordan	Kazakhstan	Kenya	Korea, South	Kosovo	Kuwait	Kyrgyzstan	Latvia	Lebanon	Liberia	Liechtenstein	Lithuania	Luxembourg	Madagascar	Malaysia	Maldives	Malta	Martinique	Mauritania	Mauritius	Mexico	Moldova	Monaco	Mongolia	Montenegro	Morocco	Mozambique	Namibia	Nepal	Netherlands	New Zealand	Nicaragua	Niger	Nigeria	North Macedonia	Norway	Oman	Pakistan	Panama	Papua New Guinea	Paraguay	Peru	Philippines	Poland	Portugal	Qatar	Romania	Russia	Rwanda	Saint Lucia	Saint Vincent and the Grenadines	San Marino	Saudi Arabia	Senegal	Serbia	Seychelles	Singapore	Slovakia	Slovenia	Somalia	South Africa	Spain	Sri Lanka	Sudan	Suriname	Sweden	Switzerland	Syria	Taiwan*	Tanzania	Thailand	Timor-Leste	Togo	Trinidad and Tobago	Tunisia	Turkey	Uganda	Ukraine	United Arab Emirates	United Kingdom	Uruguay	US	Uzbekistan	Venezuela	Vietnam	Zambia	Zimbabwe
1/22/20	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	548	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	2	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
1/23/20	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	643	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	3	0	0	0	0	0	0	0	0	0	0	1	0	0	2	0	0
1/24/20	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	920	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	0	0	0	3	0	5	0	0	0	0	0	0	0	0	0	0	2	0	0	2	0	0
1/25/20	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1406	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	2	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	0	0	0	3	0	7	0	0	0	0	0	0	0	0	0	0	2	0	0	2	0	0
1/26/20	0	0	0	0	0	0	0	0	4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2075	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	4	0	0	0	3	0	0	0	0	0	0	0	0	0	0	4	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	4	0	0	0	0	0	0	0	0	0	0	0	4	0	8	0	0	0	0	0	0	0	0	0	0	5	0	0	2	0	0

Column Name Set

library(janitor)

# Make the first row the column titles
case_by_country <- case_by_country %>%
                          row_to_names(row_number = 1 )

head(case_by_country,26)  %>% kable()

	Australia	Belgium	Cambodia	Canada	China	Cruise Ship	Egypt	Finland	France	Germany	India	Italy	Japan	Korea, South	Malaysia	Nepal	Philippines	Russia	Singapore	Spain	Sri Lanka	Sweden	Taiwan*	Thailand	United Arab Emirates	United Kingdom	US	Vietnam
1/22/20	0	0	0	0	548	0	0	0	0	0	0	0	2	1	0	0	0	0	0	0	0	0	1	2	0	0	1	0
1/23/20	0	0	0	0	643	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0	1	3	0	0	1	2
1/24/20	0	0	0	0	920	0	0	0	2	0	0	0	2	2	0	0	0	0	3	0	0	0	3	5	0	0	2	2
1/25/20	0	0	0	0	1406	0	0	0	3	0	0	0	2	2	3	1	0	0	3	0	0	0	3	7	0	0	2	2
1/26/20	4	0	0	1	2075	0	0	0	3	0	0	0	4	3	4	1	0	0	4	0	0	0	4	8	0	0	5	2
1/27/20	5	0	1	1	2877	0	0	0	3	1	0	0	4	4	4	1	0	0	5	0	1	0	5	8	0	0	5	2
1/28/20	5	0	1	2	5509	0	0	0	4	4	0	0	7	4	4	1	0	0	7	0	1	0	8	14	0	0	5	2
1/29/20	6	0	1	2	6087	0	0	1	5	4	0	0	7	4	7	1	0	0	7	0	1	0	8	14	4	0	5	2
1/30/20	9	0	1	2	8141	0	0	1	5	4	1	0	11	4	8	1	1	0	10	0	1	0	9	14	4	0	5	2
1/31/20	9	0	1	4	9802	0	0	1	5	5	1	2	15	11	8	1	1	2	13	0	1	1	10	19	4	2	7	2
2/1/20	12	0	1	4	11891	0	0	1	6	8	1	2	20	12	8	1	1	2	16	1	1	1	10	19	4	2	8	6
2/2/20	12	0	1	4	16630	0	0	1	6	10	2	2	20	15	8	1	2	2	18	1	1	1	10	19	5	2	8	6
2/3/20	12	0	1	4	19716	0	0	1	6	12	3	2	20	15	8	1	2	2	18	1	1	1	10	19	5	2	11	8
2/4/20	13	1	1	4	23707	0	0	1	6	12	3	2	22	16	10	1	2	2	24	1	1	1	11	25	5	2	11	8
2/5/20	13	1	1	5	27440	0	0	1	6	12	3	2	22	19	12	1	2	2	28	1	1	1	11	25	5	2	11	8
2/6/20	14	1	1	5	30587	0	0	1	6	12	3	2	45	23	12	1	2	2	28	1	1	1	16	25	5	2	11	10
2/7/20	15	1	1	7	34110	61	0	1	6	13	3	3	25	24	12	1	3	2	30	1	1	1	16	25	5	3	11	10
2/8/20	15	1	1	7	36814	61	0	1	11	13	3	3	25	24	16	1	3	2	33	1	1	1	17	32	7	3	11	13
2/9/20	15	1	1	7	39829	64	0	1	11	14	3	3	26	25	16	1	3	2	40	2	1	1	18	32	7	3	11	13
2/10/20	15	1	1	7	42354	135	0	1	11	14	3	3	26	27	18	1	3	2	45	2	1	1	18	32	8	8	11	14
2/11/20	15	1	1	7	44386	135	0	1	11	16	3	3	26	28	18	1	3	2	47	2	1	1	18	33	8	8	12	15
2/12/20	15	1	1	7	44759	175	0	1	11	16	3	3	28	28	18	1	3	2	50	2	1	1	18	33	8	9	12	15
2/13/20	15	1	1	7	59895	175	0	1	11	16	3	3	28	28	19	1	3	2	58	2	1	1	18	33	8	9	13	16
2/14/20	15	1	1	7	66358	218	1	1	11	16	3	3	29	28	19	1	3	2	67	2	1	1	18	33	8	9	13	16
2/15/20	15	1	1	7	68413	285	1	1	12	16	3	3	43	28	22	1	3	2	72	2	1	1	18	33	8	9	13	16
2/16/20	15	1	1	7	70513	355	1	1	12	16	3	3	59	29	22	1	3	2	75	2	1	1	20	34	9	9	13	16

I printed the head of the data frame thats been just created. The columns are now of type <fctr>, or factor. This was one of the side effects of rotating the data frame.I turned all of these columns back into doubles.

# Transforming the columns to numeric values
case_by_country <- case_by_country %>%
                           apply(MARGIN = 2, as.numeric) %>%
                           as.data.frame()

head(case_by_country,25)  %>% kable()

Australia	Belgium	Cambodia	Canada	China	Cruise Ship	Egypt	Finland	France	Germany	India	Italy	Japan	Korea, South	Malaysia	Nepal	Philippines	Russia	Singapore	Spain	Sri Lanka	Sweden	Taiwan*	Thailand	United Arab Emirates	United Kingdom	US	Vietnam
0	0	0	0	548	0	0	0	0	0	0	0	2	1	0	0	0	0	0	0	0	0	1	2	0	0	1	0
0	0	0	0	643	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0	1	3	0	0	1	2
0	0	0	0	920	0	0	0	2	0	0	0	2	2	0	0	0	0	3	0	0	0	3	5	0	0	2	2
0	0	0	0	1406	0	0	0	3	0	0	0	2	2	3	1	0	0	3	0	0	0	3	7	0	0	2	2
4	0	0	1	2075	0	0	0	3	0	0	0	4	3	4	1	0	0	4	0	0	0	4	8	0	0	5	2
5	0	1	1	2877	0	0	0	3	1	0	0	4	4	4	1	0	0	5	0	1	0	5	8	0	0	5	2
5	0	1	2	5509	0	0	0	4	4	0	0	7	4	4	1	0	0	7	0	1	0	8	14	0	0	5	2
6	0	1	2	6087	0	0	1	5	4	0	0	7	4	7	1	0	0	7	0	1	0	8	14	4	0	5	2
9	0	1	2	8141	0	0	1	5	4	1	0	11	4	8	1	1	0	10	0	1	0	9	14	4	0	5	2
9	0	1	4	9802	0	0	1	5	5	1	2	15	11	8	1	1	2	13	0	1	1	10	19	4	2	7	2
12	0	1	4	11891	0	0	1	6	8	1	2	20	12	8	1	1	2	16	1	1	1	10	19	4	2	8	6
12	0	1	4	16630	0	0	1	6	10	2	2	20	15	8	1	2	2	18	1	1	1	10	19	5	2	8	6
12	0	1	4	19716	0	0	1	6	12	3	2	20	15	8	1	2	2	18	1	1	1	10	19	5	2	11	8
13	1	1	4	23707	0	0	1	6	12	3	2	22	16	10	1	2	2	24	1	1	1	11	25	5	2	11	8
13	1	1	5	27440	0	0	1	6	12	3	2	22	19	12	1	2	2	28	1	1	1	11	25	5	2	11	8
14	1	1	5	30587	0	0	1	6	12	3	2	45	23	12	1	2	2	28	1	1	1	16	25	5	2	11	10
15	1	1	7	34110	61	0	1	6	13	3	3	25	24	12	1	3	2	30	1	1	1	16	25	5	3	11	10
15	1	1	7	36814	61	0	1	11	13	3	3	25	24	16	1	3	2	33	1	1	1	17	32	7	3	11	13
15	1	1	7	39829	64	0	1	11	14	3	3	26	25	16	1	3	2	40	2	1	1	18	32	7	3	11	13
15	1	1	7	42354	135	0	1	11	14	3	3	26	27	18	1	3	2	45	2	1	1	18	32	8	8	11	14
15	1	1	7	44386	135	0	1	11	16	3	3	26	28	18	1	3	2	47	2	1	1	18	33	8	8	12	15
15	1	1	7	44759	175	0	1	11	16	3	3	28	28	18	1	3	2	50	2	1	1	18	33	8	9	12	15
15	1	1	7	59895	175	0	1	11	16	3	3	28	28	19	1	3	2	58	2	1	1	18	33	8	9	13	16
15	1	1	7	66358	218	1	1	11	16	3	3	29	28	19	1	3	2	67	2	1	1	18	33	8	9	13	16
15	1	1	7	68413	285	1	1	12	16	3	3	43	28	22	1	3	2	72	2	1	1	18	33	8	9	13	16

Sars CoV-2

Let’s once again find the maximum number of cases reported in the US.

# Find the maximum number of confirmed cases in the US
case_by_country %>%
  select(US) %>%
  max()

## [1] 33272

Visualization

I’ve build a line graph showing the number of confirmed cases over time for a particular country. To do this, first I need to add a new column to dataset to represent the date. The first day in our dataset was January 22nd. Let’s represent that as day 1. January 23rd would then be day 2, and so on.

# Adding the date column
nrow(case_by_country)

## [1] 61

case_by_country <- case_by_country %>%
                        mutate(date = 1:61)

head(case_by_country, 28) %>% kable()

Australia	Belgium	Cambodia	Canada	China	Cruise Ship	Egypt	Finland	France	Germany	India	Italy	Japan	Korea, South	Malaysia	Nepal	Philippines	Russia	Singapore	Spain	Sri Lanka	Sweden	Taiwan*	Thailand	United Arab Emirates	United Kingdom	US	Vietnam	date
0	0	0	0	548	0	0	0	0	0	0	0	2	1	0	0	0	0	0	0	0	0	1	2	0	0	1	0	1
0	0	0	0	643	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0	1	3	0	0	1	2	2
0	0	0	0	920	0	0	0	2	0	0	0	2	2	0	0	0	0	3	0	0	0	3	5	0	0	2	2	3
0	0	0	0	1406	0	0	0	3	0	0	0	2	2	3	1	0	0	3	0	0	0	3	7	0	0	2	2	4
4	0	0	1	2075	0	0	0	3	0	0	0	4	3	4	1	0	0	4	0	0	0	4	8	0	0	5	2	5
5	0	1	1	2877	0	0	0	3	1	0	0	4	4	4	1	0	0	5	0	1	0	5	8	0	0	5	2	6
5	0	1	2	5509	0	0	0	4	4	0	0	7	4	4	1	0	0	7	0	1	0	8	14	0	0	5	2	7
6	0	1	2	6087	0	0	1	5	4	0	0	7	4	7	1	0	0	7	0	1	0	8	14	4	0	5	2	8
9	0	1	2	8141	0	0	1	5	4	1	0	11	4	8	1	1	0	10	0	1	0	9	14	4	0	5	2	9
9	0	1	4	9802	0	0	1	5	5	1	2	15	11	8	1	1	2	13	0	1	1	10	19	4	2	7	2	10
12	0	1	4	11891	0	0	1	6	8	1	2	20	12	8	1	1	2	16	1	1	1	10	19	4	2	8	6	11
12	0	1	4	16630	0	0	1	6	10	2	2	20	15	8	1	2	2	18	1	1	1	10	19	5	2	8	6	12
12	0	1	4	19716	0	0	1	6	12	3	2	20	15	8	1	2	2	18	1	1	1	10	19	5	2	11	8	13
13	1	1	4	23707	0	0	1	6	12	3	2	22	16	10	1	2	2	24	1	1	1	11	25	5	2	11	8	14
13	1	1	5	27440	0	0	1	6	12	3	2	22	19	12	1	2	2	28	1	1	1	11	25	5	2	11	8	15
14	1	1	5	30587	0	0	1	6	12	3	2	45	23	12	1	2	2	28	1	1	1	16	25	5	2	11	10	16
15	1	1	7	34110	61	0	1	6	13	3	3	25	24	12	1	3	2	30	1	1	1	16	25	5	3	11	10	17
15	1	1	7	36814	61	0	1	11	13	3	3	25	24	16	1	3	2	33	1	1	1	17	32	7	3	11	13	18
15	1	1	7	39829	64	0	1	11	14	3	3	26	25	16	1	3	2	40	2	1	1	18	32	7	3	11	13	19
15	1	1	7	42354	135	0	1	11	14	3	3	26	27	18	1	3	2	45	2	1	1	18	32	8	8	11	14	20
15	1	1	7	44386	135	0	1	11	16	3	3	26	28	18	1	3	2	47	2	1	1	18	33	8	8	12	15	21
15	1	1	7	44759	175	0	1	11	16	3	3	28	28	18	1	3	2	50	2	1	1	18	33	8	9	12	15	22
15	1	1	7	59895	175	0	1	11	16	3	3	28	28	19	1	3	2	58	2	1	1	18	33	8	9	13	16	23
15	1	1	7	66358	218	1	1	11	16	3	3	29	28	19	1	3	2	67	2	1	1	18	33	8	9	13	16	24
15	1	1	7	68413	285	1	1	12	16	3	3	43	28	22	1	3	2	72	2	1	1	18	33	8	9	13	16	25
15	1	1	7	70513	355	1	1	12	16	3	3	59	29	22	1	3	2	75	2	1	1	20	34	9	9	13	16	26
15	1	1	8	72434	454	1	1	12	16	3	3	66	30	22	1	3	2	77	2	1	1	22	35	9	9	13	16	27
15	1	1	8	74211	542	1	1	12	16	3	3	74	31	22	1	3	2	81	2	1	1	22	35	9	9	13	16	28

Let’s now see the number of cases in Bangladesh over the days in the dataset.

library(ggplot2)
# Creating a line graph with date on the X axis and number of cases in Bangladesh on the Y axis
case_by_country %>% ggplot(aes(x = date, y = Bangladesh)) +
                      geom_line()

That line of code is pretty concise. Having a column containing only the confirmed cases from a particular country made this graph relatively simple to create.

Finally, I did a bit of work to add title.

# Adding a proper title, x label, and y label
case_by_country %>% ggplot(aes(x = date, y = Bangladesh, color = Bangladesh )) +
                      geom_line() +
                      labs(x = "Number of days since January 22nd, 2020", y = "Number of confirmed cases", title = "Covid-19 Confirmed cases In Bangladesh")

Author github: Emon-ProCoder7 2. Author Linked-in:Md Tabassum Hossain Emon ↩

Investigating the Coronavirus Pandemic

Md. Tabassum Hossain Emon ¹

6/12/2020

Investigation of the Data

Looking At March 22nd, 2020

Filtering By Values

Grouping By Country

Transposing Data Frames

Column Name Set

Visualization

Investigating the Coronavirus Pandemic

Md. Tabassum Hossain Emon 1

6/12/2020

Investigation of the Data

Looking At March 22nd, 2020

Filtering By Values

Grouping By Country

Transposing Data Frames

Column Name Set

Visualization

Md. Tabassum Hossain Emon ¹